基于空间谱非局部均值算法的多能锥束ct重建。

IF 2.1 3区 数学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
SIAM Journal on Imaging Sciences Pub Date : 2018-01-01 Epub Date: 2018-05-08 DOI:10.1137/17M1123237
Bin Li, Chenyang Shen, Yujie Chi, Ming Yang, Yifei Lou, Linghong Zhou, Xun Jia
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引用次数: 15

摘要

多能计算机断层扫描(CT)是一种新兴的医学图像模式,在诊断和治疗方面具有许多潜在的应用。然而,较高的系统成本和技术壁垒阻碍了其进入常规临床。在这项研究中,我们提出了一个框架来实现多能锥束CT (ME-CBCT)在CBCT系统上广泛使用,并已常规用于放疗图像引导。在我们的方法中,实现了kVp切换技术,该技术获得了kVp水平在多个值之间循环的x射线投影。对于这种基于kvp开关的ME-CBCT采集,每个能量通道的x射线投影只是所有采集投影的一个子集。这导致了采样不足的问题,对重建问题提出了挑战。我们提出了一种空间光谱非局部均值(ssNLM)方法来重建ME-CBCT,该方法利用沿空间和光谱方向的图像相关性来抑制噪声和条纹伪影。为了解决不同能量通道的强度尺度差异,采用了直方图匹配方法。该方法与传统NLM方法的不同之处在于,它包含了光谱维度,有助于有效地去除在不同能量通道的图像中出现在不同方向的条纹伪影。给出了算法的收敛性分析。一组全面的仿真和真实实验研究证明了我们的ME-CBCT方案的可行性,并且与传统的滤波反投影(FBP)和NLM重建方法相比,能够获得更好的图像质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

MULTI-ENERGY CONE-BEAM CT RECONSTRUCTION WITH A SPATIAL SPECTRAL NONLOCAL MEANS ALGORITHM.

MULTI-ENERGY CONE-BEAM CT RECONSTRUCTION WITH A SPATIAL SPECTRAL NONLOCAL MEANS ALGORITHM.

MULTI-ENERGY CONE-BEAM CT RECONSTRUCTION WITH A SPATIAL SPECTRAL NONLOCAL MEANS ALGORITHM.
Multi-energy computed tomography (CT) is an emerging medical image modality with a number of potential applications in diagnosis and therapy. However, high system cost and technical barriers obstruct its step into routine clinical practice. In this study, we propose a framework to realize multi-energy cone beam CT (ME-CBCT) on the CBCT system that is widely available and has been routinely used for radiotherapy image guidance. In our method, a kVp switching technique is realized, which acquires x-ray projections with kVp levels cycling through a number of values. For this kVp-switching based ME-CBCT acquisition, x-ray projections of each energy channel are only a subset of all the acquired projections. This leads to an undersampling issue, posing challenges to the reconstruction problem. We propose a spatial spectral non-local means (ssNLM) method to reconstruct ME-CBCT, which employs image correlations along both spatial and spectral directions to suppress noisy and streak artifacts. To address the intensity scale difference at different energy channels, a histogram matching method is incorporated. Our method is different from conventionally used NLM methods in that spectral dimension is included, which helps to effectively remove streak artifacts appearing at different directions in images with different energy channels. Convergence analysis of our algorithm is provided. A comprehensive set of simulation and real experimental studies demonstrate feasibility of our ME-CBCT scheme and the capability of achieving superior image quality compared to conventional filtered backprojection-type (FBP) and NLM reconstruction methods.
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来源期刊
SIAM Journal on Imaging Sciences
SIAM Journal on Imaging Sciences COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, SOFTWARE ENGINEERING
CiteScore
3.80
自引率
4.80%
发文量
58
审稿时长
>12 weeks
期刊介绍: SIAM Journal on Imaging Sciences (SIIMS) covers all areas of imaging sciences, broadly interpreted. It includes image formation, image processing, image analysis, image interpretation and understanding, imaging-related machine learning, and inverse problems in imaging; leading to applications to diverse areas in science, medicine, engineering, and other fields. The journal’s scope is meant to be broad enough to include areas now organized under the terms image processing, image analysis, computer graphics, computer vision, visual machine learning, and visualization. Formal approaches, at the level of mathematics and/or computations, as well as state-of-the-art practical results, are expected from manuscripts published in SIIMS. SIIMS is mathematically and computationally based, and offers a unique forum to highlight the commonality of methodology, models, and algorithms among diverse application areas of imaging sciences. SIIMS provides a broad authoritative source for fundamental results in imaging sciences, with a unique combination of mathematics and applications. SIIMS covers a broad range of areas, including but not limited to image formation, image processing, image analysis, computer graphics, computer vision, visualization, image understanding, pattern analysis, machine intelligence, remote sensing, geoscience, signal processing, medical and biomedical imaging, and seismic imaging. The fundamental mathematical theories addressing imaging problems covered by SIIMS include, but are not limited to, harmonic analysis, partial differential equations, differential geometry, numerical analysis, information theory, learning, optimization, statistics, and probability. Research papers that innovate both in the fundamentals and in the applications are especially welcome. SIIMS focuses on conceptually new ideas, methods, and fundamentals as applied to all aspects of imaging sciences.
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